SeqGeq™ [seek-geek] analyzes gene expression data—particularly from single-cell RNA sequencing. SeqGeq helps you cluster and subset populations of cells, navigate this data stream using gene sets, and rapidly produce reports and visualizations.
SeqGeq™ [seek-geek] analyzes gene expression data—particularly from single-cell RNA sequencing (scRNA-seq). SeqGeq helps you cluster and subset populations of cells, navigate this data stream using gene sets, and rapidly produce reports and visualizations.
For closed captions follow this link: https://flowjo.wistia.com/medias/ijdxna8mip
Follow along with Director of Product Innovation, Ian Taylor, in this series of advanced SeqGeq webinars on four major lessons in SeqGeq: quality control, dimensionality reduction, clustering, and DEG analysis.
Example analysis of single-cell protein expression, and whole transcriptome in a combined data matrix.
Introduction to finding differentially expressed GeneSets between populations of interest using SeqGeq's volcano plots.
For closed captions follow this link: https://flowjo.wistia.com/medias/mvfz4fz6i1
Making sense of differentially expressed GeneSets from known and unknown populations using GeneSet Libraries.
"Genomic Cytometry: Using Multi-Omic Approaches to Increase Dimensionality in Cytometry" was an Invited Tutorial given at the 2019 CYTO conference for the the International Society for the Advancement of Cytometry on the 22nd May 2019.
This tutorial explores why the emerging field of Genomic Cytometry, (i.e. the measurement of cells using genomic techniques such as sequencing)—in conjunction with more traditional cytometry techniques such as fluorescence, mass and imaging cytometry—is becoming a standard tool for biologists looking to unravel complex cellular processes and to develop a deeper understanding of heterogeneity.
